A planning system must reason about the uncertainty of continuous variables in order to accurately project the possible system state over time. Prior approaches to planning under uncertainty reason about discrete possible outcomes but there has been little attention given to continuous possible outcomes. A method is devised for directly reasoning about the uncertainty in continuous activity duration and resource usage for planning problems.By representing random variables as parametric distributions, computing projected system state can be simplified in some cases. Common approximation and novel methods are compared for over-constrained and lightly constrained domains. The system compares a few common approximation methods for an iterative repair planner. Results show improvements in robustness over the conventional nonprobabilistic representation by reducing the number of conflicts witnessed by execution. The improvement is more significant for larger problems and problems with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels.